Age-specific fertility rates (ASFRs) provide the most extensive record of reproductive change, but their aggregate nature obscures the individual-level behavioral mechanisms that drive fertility trends. To bridge this micro-macro divide, we introduce a likelihood-free Bayesian framework that couples a demographically interpretable, individual-level simulation model of the reproductive process with Sequential Neural Posterior Estimation (SNPE). We show that this framework successfully recovers core behavioral parameters governing contemporary fertility, including preferences for family size, reproductive timing, and contraceptive failure, using only ASFRs. The framework's effectiveness is validated on cohorts from four countries with diverse fertility regimes. Most compellingly, the model, estimated solely on aggregate data, successfully predicts out-of-sample distributions of individual-level outcomes, including age at first sex, desired family size, and birth intervals. Because our framework yields complete synthetic life histories, it significantly reduces the data requirements for building microsimulation models and enables behaviorally explicit demographic forecasts.

We introduce and study the weakly single-crossing domain on trees which is a generalization of the well-studied single-crossing domain in social choice theory. We design a polynomial-time algorithm for recognizing preference profiles which belong to this domain. We then develop an efficient elicitation algorithm for this domain which works even if the preferences can be accessed only sequentially and the underlying single-crossing tree structure is not known beforehand. We also prove matching lower bound on the query complexity of our elicitation algorithm when the number of voters is large compared to the number of candidates. We also prove a lower bound of $Ω(m^2\log n)$ on the number of queries that any algorithm needs to ask to elicit single crossing profile when random queries are allowed. This resolves an open question in an earlier paper and proves optimality of their preference elicitation algorithm when random queries are allowed.

As large language models (LLMs) are increasingly deployed as autonomous agents, understanding their cooperation and social mechanisms is becoming increasingly important. In particular, how LLMs balance self-interest and collective well-being is a critical challenge for ensuring alignment, robustness, and safe deployment. In this paper, we examine the challenge of costly sanctioning in multi-agent LLM systems, where an agent must decide whether to invest its own resources to incentivize cooperation or penalize defection. To study this, we adapt a public goods game with institutional choice from behavioral economics, allowing us to observe how different LLMs navigate social dilemmas over repeated interactions. Our analysis reveals four distinct behavioral patterns among models: some consistently establish and sustain high levels of cooperation, others fluctuate between engagement and disengagement, some gradually decline in cooperative behavior over time, and others rigidly follow fixed strategies regardless of outcomes. Surprisingly, we find that reasoning LLMs, such as the o1 series, struggle significantly with cooperation, whereas some traditional LLMs consistently achieve high levels of cooperation. These findings suggest that the current approach to improving LLMs, which focuses on enhancing their reasoning capabilities, does not necessarily lead to cooperation, providing valuable insights for deploying LLM agents in environments that require sustained collaboration. Our code is available at https://github.com/davidguzmanp/SanctSim

This flurry of actions made for glitzy press moments, including an hour-long speech from the president and onstage signings. But while the tech industry cheered these announcements (which will swell their coffers), they obscured the fact that the administration is currently decimating the very policies that enabled America to become the world leader in AI in the first place. To maintain America's leadership in AI, you have to understand what produced it. Here are four specific long-standing public policies that helped the US achieve this leadership--advantages that the administration is undermining. Generative AI products released recently by American companies, like ChatGPT, were developed with industry-funded research and development.

President Donald Trump wants to ensure the US government only gives federal contracts to artificial intelligence developers whose systems are "free from ideological bias". But the new requirements could allow his administration to impose its own worldview on tech companies' AI models – and companies may face significant challenges and risks in trying to modify their models to comply. "The suggestion that government contracts should be structured to ensure AI systems are'objective' and'free from top-down ideological bias' prompts the question: objective according to whom?" says Becca Branum at the Center for Democracy & Technology, a public policy non-profit in Washington DC. The Trump White House's AI Action Plan, released on 23 July, recommends updating federal guidelines "to ensure that the government only contracts with frontier large language model (LLM) developers who ensure that their systems are objective and free from top-down ideological bias". Trump signed a related executive order titled "Preventing Woke AI in the Federal Government" on the same day.

It found that the security giant Evolv lied about the accuracy of its AI-powered security checkpoints, which are used in stadiums and schools but failed to catch a seven-inch knife that was ultimately used to stab a student. It went after the facial recognition company Intellivision, saying the company made unfounded claims that its tools operated without gender or racial bias. It fined startups promising bogus "AI lawyer" services and one that sold fake product reviews generated with AI. These actions did not result in fines that crippled the companies, but they did stop them from making false statements and offered customers ways to recover their money or get out of contracts. In each case, the FTC found, everyday people had been harmed by AI companies that let their technologies run amok.

Retired Air Force Gen. Charles Wald joins'Fox News Live' to weigh in on Russia's increased attacks on Ukraine despite President Donald Trump's ultimatum to Vladimir Putin. Russia and Ukraine took aim at corresponding Black Sea resort cities early Thursday morning, just hours after ceasefire talks in Turkey once again failed to deliver results. The major Russian resort city of Sochi was rocked by a Ukrainian drone strike that began around 1 a.m. and lasted until 3 a.m., where one person was reportedly killed and another injured, according to Ukrainian media outlet the Kyiv Independent, though the Ukrainian military has not commented on the incident. An oil depot in the Krasnodar Krai region where Sochi is located was also struck, though the extent of the damage remains unclear. Russia's President Vladimir Putin chairs a meeting via a video conference at the Kremlin in Moscow on July 23, 2025.

But Peskov poured cold water on the idea, saying it was "premature" for the two presidents to meet. "They [Ukraine] are trying to put the cart slightly ahead of the horse," he said, adding much more work had to be done before any such meeting could take place. Ukrainian MP Oleksiy Hocharenko said on Facebook that a separate meeting between Umerov and Medinsky had taken place behind closed doors on the sidelines of the main talks. Hocharenko said Umerov and Medinsky have a "good relationship". The first two rounds of ceasefire talks were held in May and June at the request of US President Donald Trump, who has repeatedly said he wants to see the end of the "horrible, bloody war" that was sparked by Russia's invasion of Ukraine in 2022.

An NYPD drone observed four minors, between the ages of 12 and 16 years old, riding on top of a train in the Bronx on Thursday as it passed multiple stations at a high speed. FIRST ON FOX: A new move by Congress would unleash civilian drone use across America's skies by establishing rules to allow them to be flown beyond a user's line of sight and using AI for approval to do so. Her LIFT Act, introduced in the House on Thursday, would require Transportation Secretary Sean Duffy to establish set performance and safety standards for BVLOS operations and review current aviation standards, which were designed with manned aircraft in mind. It would also require the Transportation secretary to deploy artificial intelligence to assist with processing waiver applications to allow civilian drones to fly BVLOS. Industry operators have long pushed for new BVLOS policy to replace the current system in which individuals must apply for waivers with the Federal Aviation Adminsitration (FAA) through a costly, cumbersome process to fly beyond the line of sight.

Chinese-made engines are being covertly shipped via front companies to a state-owned drone manufacturer in Russia, labeled as "industrial refrigeration units" to avoid detection in the wake of Western sanctions, according to three European security officials and documents. The shipments have allowed Russian weapons-maker IEMZ Kupol to increase its production of the Garpiya-A1 attack drone, despite the U.S. and EU sanctions imposed in October designed to disrupt its supply chain, according to the sources and documents, which included contracts, invoices and customs paperwork. An internal Kupol document showed it signed a contract with the Russian defense ministry to produce more than 6,000 Garpiya this year, up from 2,000 in 2024. The document stated that more than 1,500 drones had already been delivered by April.